FOCUS Data Sharing Plan

• Per NIH Data Sharing Policy, data from this project, including questionnaire data, de-identified demographics and disease information, as well as biomarker data will be shared at the completion of the study via computerized dataset.  We do not plan to release data pertaining to participants’ individual web-usage as we believe this data to be private and not essential to the overarching purposes or goals of the project. However, we will release summary statistics pertaining to usage (total app usage, frequency of home practice) so Aim 2 of this study can be evaluated by others interested in the web-usage and descriptive features of the study. 

• A study codebook will be provided outlining the methodology and procedures used to collect the data, variable definitions and labels, scored variable information, and information on the general structure/timepoint labeling of the available data. Any data transformations will be included as well as a written description of why/how this was done to normalize any non-normal distributions. project codebook 

 

2.     Related Tools, Software and/or Code

• For this project we will be using MPlus and SAS for our analyses. MPlus can be accessed at https://www.statmodel.com/.  SAS is available at https://www.sas.com/en_us/software/stat.html

• We will be using Actiwatch Spectrum Plus to collect our physiologic sleep data. The raw data from the watches will be supplied in requested datasets.
  
   

3.     Standards

• The trial will be evaluated by our Cancer IRB for approval and will be registered at clinicaltrials.gov

• CONSORT E-Health will be used to monitor accrual, retention, and attrition.

• We will utilize best practices for behavior change study treatment fidelity of NIH Behavior Change Consortium.

• Randomization scheme will be blinded to participants, research assistants and statistician. PI will not be blinded as she will be supervising post-doctoral fellow work with participants.

• We will employ the FDA E6 Good Clinical Practice and Good Laboratory Practice Guidelines to ensure data quality management. 

• Our research team will conduct weekly recorded research data-focused meetings where we review study recruitment, problem solve research process issues, and review study reports from RedCap (OSU encrypted virtual data collection system) and Moby to ensure questionnaires are completed in a timely and valid manner. 

• Data will be checked on a weekly basis to identify any missing data issues that could be corrected in a reasonable timeframe via patient contact. 

• All medical record information will be verified by a second research assistant. Descriptive analyses will be completed monthly to identify and correct any anomalies in the data. The rationale for all data changes will be recorded in the data log kept by the Project Coordinator and research assistants.
  
   

4.     Data preservation, access, and associated timelines

• Data will be shared with the National Sleep Research Resource (https://www.sleepdata.org), Cancer Data Services (https://datacommons.cancer.gov/repository/cancer-data-service#project-datatype), and The Immunology Database and Analysis Portal (https://www.immport.org/home)

• We will release data in “waves” relating to publications deriving from this data. For instance, our trial sleep data will be released after Aim 1 is published, our immunology data after Aim 3 is published, and all other data will be available after study is completed via Cancer Data Services.

• Data will remain available for 15 years. After this time, treatment advances may affect how these diseases are treated and/or symptoms are addressed.
  
   

5.     Access, distribution, or reuse considerations 

• Data will be redacted to strip all direct and indirect identifiers and we will work to minimize risks of unauthorized disclosures of personal identifiers consistent with the Privacy Rule, a Federal regulation under HIPAA.

• We will utilize a data sharing agreement that requires 1) a commitment to using the data and codebook only for research purposes, 2) a commitment to securing the data via appropriate computer technology, and 3) a commitment to destroying or returning the data after analyses are complete.  
  
   

6.     Oversight of data management and sharing

• Compliance with the DMS Plan will be monitored and managed by Dr. Wells-Di Gregorio, PI in collaboration with our three project statisticians. Statisticians will attend our monthly meeting and will be apprised of any data issues as they arise. We will monitor data weekly to ensure accuracy and address missing data issues in a timely manner when possible.